Scaling Policy Gradients for Reinforcement Learning in Robotics - Part 1

This lecture from the Montreal Robotics series explores reinforcement learning (RL) in robotics contexts, with a deep focus on policy gradients and their real-world applications. Learn about autonomous cleaning robots as practical examples of scaled RL implementation. Discover how policy gradients optimize reward functions without explicitly modeling environmental dynamics, and examine different policy distributions including cross-entropy for discrete actions and Gaussian distributions for continuous actions through interactive demonstrations. Gain insights into the mathematical foundations of policy gradients, understanding their sampling-based approach and how policy parameters are optimized to maximize expected returns. The lecture includes a comprehensive walkthrough of a robotics dataset homework assignment using Google Colab, covering essential techniques for data processing, standardization, and model training, with specific guidance on challenges like action dimension scaling. Access the accompanying materials on GitHub to practice implementing these concepts.